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Project abstract for group bittermp
Systems Biology of Cancer and Fibrosis
Organ function depends upon a connective tissue stromal network, consisting of fibroblasts and their extracellular matrix (ECM) products, that imparts topographic integrity by precisely ordering constituent cellular and tissue compartments. Stromal pathology is the signature of human diseases ranging from cardiovascular and pulmonary disease to cancer. One central unresolved issue in stromal disorders is the extent to which disease progression results from intrinsically diseased fibroblasts, a diseased ECM that corrupts normal fibroblasts or collaboration between the two.
These researchers are performing a genome-wide comparison of primary human fibroblast gene expression (derived from fibrotic or control tissue) cultured on decellularized tissue ECM (fibrotic or control). Focusing on ribosome recruitment to mRNA - the most downstream step in the gene expression pathway that can be examined genome-wide - they will build a mathematical model that explains observed patterns of gene expression based on the activity of RNA regulatory elements and their trans-binding partners. They will refine the model so it can accurately predict gene expression changes in response to perturbations of RNA element-binding partner function and fibroblast ECM receptors. Their hypothesis is that fibrotic ECM can reprogram normal fibroblasts to manifest a fibrotic phenotype; and that this reprogramming will manifest as organized changes in ribosome recruitment to mRNA genome-wide.
This project has two specific aims: 1) Characterize multi-level genome-wide expression profiles in lung fibroblasts (IPF and control) on decellularized lung ECM (IPF and control) to identify how gene expression is affected based on cell type, ECM type and the interaction between cell and ECM type; and 2) Modulate ECM receptor expression to determine which ECM-receptors determine the gene expression patterns observed, and integrate these data into the model. If successful, this mathematical model will be the first to accurately predict ribosome recruitment patterns in health and disease, and unveil clues about stromal biology that can accelerate therapeutic breakthroughs in tissue fibrosis and cancer.